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US20030087456A1 - Within-sample variance classification of samples - Google Patents

Within-sample variance classification of samples Download PDF

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Publication number
US20030087456A1
US20030087456A1 US10/262,692 US26269202A US2003087456A1 US 20030087456 A1 US20030087456 A1 US 20030087456A1 US 26269202 A US26269202 A US 26269202A US 2003087456 A1 US2003087456 A1 US 2003087456A1
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Prior art keywords
sample
determining
variance
classification
radiation
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US10/262,692
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English (en)
Inventor
Howland Jones
Craig Gardner
Edward Hull
Kristin Nixon
M. Robinson
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Rio Grande Medical Technologies Inc
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Rio Grande Medical Technologies Inc
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Priority to US10/262,692 priority Critical patent/US20030087456A1/en
Priority to EP02768970A priority patent/EP1444504A1/fr
Priority to PCT/US2002/031641 priority patent/WO2003031954A1/fr
Assigned to INLIGHT SOLUTIONS, INC. reassignment INLIGHT SOLUTIONS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HULL, EDWARD L., JONES, HOWLAND D. T., NIXON, KRISTIN A., ROBINSON, M. RIES, GARDNER, CRAIG M.
Publication of US20030087456A1 publication Critical patent/US20030087456A1/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to spectral analysis of samples to determine if the samples are normal or abnormal or to otherwise classify the sample. More specifically, the present invention relates to classification of a biological sample on the basis of attenuation of infrared radiation at different wavelengths using a within-sample variance model.
  • Infrared spectroscopy is sensitive to the rotational and vibrational energy levels of bonds, functional groups and molecules.
  • the spectrum of a tissue sample thus contains information about the biochemical and morphological make-up of the sample. This information can be used to separate cells or tissues into classes according to some descriptive difference, such as cell type or disease status.
  • Infrared spectroscopy offers the advantages of rapid, non-destructive, and automated testing using relatively inexpensive and robust equipment, all of which lead to cost-effective measurements.
  • a simple univariate measure such as the peak height of an absorbance band can be used for classification.
  • sophisticated multivariate techniques such as principal component analysis can combine the spectral values at many different wavelengths of light to provide classification ability.
  • a classification model such as linear discriminant analysis is generated (or trained) from a set of spectral data taken from samples with known class assignments determined from an accurate, “gold standard” reference method.
  • the goal of model generation is to seek some relationship (defined by the type of algorithm being used) between the spectral data and the known classes. This model is then used to predict the classes of new (test) samples. Comparing the classes predicted by the algorithm to the known classes provides estimates of the algorithm accuracy.
  • the present invention comprises systems and methods for classifying a sample utilizing spectral analysis.
  • a “sample” refers to what is being classified, for example, a sample can comprise a group of cells from an individual, collected from one or more collection sites and at one or more collection times; a sample can comprise cells from a group of individuals (where the group is to be classified); a sample can comprise extracts from one or more fluids to be classified; a sample can comprise tissue measured in vivo.
  • “Classifying samples” includes determination of any property of the sample, including, as examples, membership in one or more classes, analyte concentration in the sample, and presence or extent of a particular material or property. Variance in response to radiation within a single sample can allow classification of a sample.
  • the variance is often discussed herein in terms of variance among regions of a sample, where a “region” refers to a distinguishable determination of the response to radiation. Examples of regions include different spatial portions of a sample, different times for determination of a response, and different preparation methods applied before determining a response (e.g., a single cell collection event, followed by preparation of subsets of the collected cells in different manners).
  • the present invention contemplates a single treatment of within-sample variance, and the combination of multiple treatments of within-sample variance for classification.
  • the present invention also contemplates combining classification models, for example, combining a within-sample variance classification with other classification methods.
  • a system can comprise means for generating light at a plurality of different wavelengths.
  • the system can further comprise means for directing at least a portion of the generated light into a plurality of regions of a sample (e.g., cells in a biological sample).
  • a sample e.g., cells in a biological sample.
  • each region has an area of from about 100 ⁇ m 2 to about half the sample area. In a prepared slide, this would include from a fraction of a cell to many cells.
  • the system can further comprise means for collecting at least a portion of the infrared light after it has interacted with each region. Means for determining the intensity of the collected infrared light for each region are included, with the intensity determined as a function of the wavelength.
  • the system can also comprise means for storing a within-sample variance classification model which contains data indicative of a correct classification of known sample variances.
  • a processor means is coupled to the means for determining the measured intensities and the means for storing the model. The processor means determines the classification of the sample as one of two or more types by use of the within-sample variance classification model and the measured intensities for each region.
  • the stored classification model can be of various types related to the variance among the regions.
  • One embodiment comprises a sample standard deviation model.
  • Other embodiments comprise a sample mean absolute deviation model or a sample median absolute deviation model.
  • a biological sample comprising a plurality of cells can be provided.
  • the sample presents a substantially monocellular layer such as a sample prepared by the cytospin cell preparation technique or Cytyc Corporation's ThinPrep.
  • Infrared light at a plurality of different wavelengths is generated.
  • the infrared light irradiates a plurality of regions of a biological sample and an optical characteristic of each region determined.
  • An optical characteristic is a property of how the region interacts with incident radiation, for example absorption, reflection, scattering, transmission, Raman effects, optical path lengths, and combinations thereof.
  • An optical characteristic determined at a plurality of different incident radiation properties comprises a sample response spectrum.
  • the optical characteristics of at least two of the plurality of regions can be used to classify the sample as one of two or more types, using a within-sample variance classification model. Examples of a within-sample variance classification model include a sample standard deviation model, a sample mean absolute deviation model, and a sample median absolute deviation model. Further, additional models can be applied to the spectral data to improve the accuracy of the classification.
  • FIG. 1 is a schematic diagram of an apparatus useful in conducting the classifications contemplated by this invention.
  • FIG. 2 is a flow chart of how samples were accepted into a study and how “gold standard” reference values were determined for those accepted samples.
  • FIG. 3 is a schematic of model building, model validation, and bundling.
  • FIG. 5 is an AUC performance metric for each of the 229 individual model treatments generated from within-sample spectral standard deviation data.
  • FIG. 8 is an AUC performance metric for each of the 573 individual model treatments generated from within-sample spectral standard deviation data, within-sample spectral mean data and individual cell spectral data.
  • FIG. 1 is a schematic representation of an example apparatus according to the present invention.
  • a radiation source ( 9 ) supplies radiation to a collimating mirror ( 7 ).
  • the collimated beam travels to beamsplitter ( 10 ) which is the beamsplitter of a Michelson interferometer.
  • the beam is split into two beams which travel to two end mirrors of the interferometer ( 12 ) and ( 12 ′).
  • Mirror ( 12 ) is the fixed mirror and mirror ( 12 ′) is the moving mirror of the interferometer.
  • the beams then return to beamsplitter ( 10 ) where they recombine and exit towards mirror ( 11 ).
  • Mirror ( 11 ) focuses the beam onto aperture ( 17 ), the size of which is adjustable.
  • the beam then travels to focusing mirror ( 15 ) which re-images aperture ( 17 ) onto the specimen ( 23 ).
  • Specimen ( 23 ) is mounted on a moving stage so that it can move in a plane perpendicular to the beam axis.
  • Plan view ( 30 ) is a representation of a specimen conceptually separated into different regions or portions.
  • a method for classifying a sample includes providing a sample that can be interrogated over a plurality of regions, for example, a sample comprising a plurality of cells spread over an area of a biological sample.
  • the method can further include generating a plurality of different wavelengths of light and irradiating a plurality of regions of the sample with the plurality of different wavelengths. Intensity attenuations due to each region's interaction with the light can be measured to obtain a sample response spectrum comprising intensity information at multiple wavelengths for each of at least two of the plurality of regions.
  • the sample can then be classified as one of two or more types from the measured intensity attenuations using a within-sample variance classification model.
  • the mean absolute deviation is the average of the absolute values of the data centered by the mean.
  • the median absolute deviation is the median of the absolute value of the data centered by the median.
  • the statistic referred to as the variance is the mean value of the squares of the data centered by the mean of the data.
  • population variance is as defined above for a population of data values. If a random sample of n data values (X 1 , . . .
  • Mid-infrared MIR
  • NIR Near-infrared
  • VIS visible
  • the number of regions of the sample can be selected to obtain a reliable estimate of variation based on statistics. Generally, more regions lead to more accurate determination of the variances.
  • the number of regions can be from 2 to many. As an example, in a cervical cancer screening application, from 10 to 50 regions can be suitable.
  • the area of each region can be large enough to obtain meaningful sample information; as an example, in classifying a sample comprising a plurality of cells, regions larger than one cell (e.g., an area large enough to include a plurality of cells) can be suitable.
  • Each region can include a fraction of a cell to a number of cells conducive to obtaining a reliable estimate of variation based on statistics. When the number of cells to be measured is determined, the dimensions of the regions can be determined.
  • the regions can have areas from about 100 ⁇ m 2 to about 150 mm 2 .
  • the sample can be classified as one of two or more types based on the measured intensity attenuations.
  • Table 1 shows some examples of classifications useful in some applications. TABLE 1 normal or abnormal For cancer screening/diagnosis and process monitoring normal, hyperplastic, dysplastic or For cancer screening/diagnosis neoplastic within normal limits, squamous intra- For cervical cancer screening/ epithelial lesion (high or low grade), diagnosis or carcinoma in-situ benign, pre-malignant, malignant For cancer screening/diagnosis Normal or In Need of Further Review For cancer screening/diagnosis male or female For gender screening hemolytic, lipemic or icteric For serum samples normal, prediabetic, or diabetic For screening or diagnosis of diabetes
  • HPV Human Papiloma Virus
  • a true is the actual absorbance spectrum
  • T true is the actual cellular transmission spectrum
  • T cell is the measured cellular transmittance spectrum
  • f is the fraction of the aperture area not occupied by the cell
  • T bgd is the measured background spectrum.
  • Model Building The following sections on model building and validation are illustrated in FIG. 3 (up to bundling level 1).
  • a linear discriminant analysis (LDA) classification algorithm was used to generate the various multivariate classification models.
  • Other classification models can also be suitable, including, as examples, quadratic discriminant analysis (QDA), neural networks, unsupervised classification, classification and regression trees (CART), k-nearest neighbors, and combinations thereof.
  • QDA quadratic discriminant analysis
  • CART classification and regression trees
  • the explanatory (predictor) variables were the scores of the spectra, and the dependent variable (class) was the binary normal or abnormal reference value from each sample.
  • the LDA algorithm assumes the distribution of variables within each class is multivariate normal; it estimates the within-class mean value of each variable, and the covariance matrix between the different variables of all training samples.
  • Model Validation When predicting the class of a validation (test) sample, we used the scores generated from within-sample spectral standard deviation as the input to our linear discriminant classifier. The output of our classifier was the posterior probability (PP) that the sample belonged to the normal class.
  • PP posterior probability
  • a sample's posterior probability is the classification model's estimate of the probability that the sample in question belongs to a given class. For example, a WNL PP of 0.9 means that there is a 90% probability that the sample belongs to the class of normal samples. The quantity 1-PP is therefore the probability that the sample belongs to the abnormal class. Due to the limited number of samples in our study, a bootstrapping algorithm was used to generate a set of 13 PPs for each of the 56 samples as follows (see FIG. 3).
  • Table 2 lists the elements varied to produce the different model treatments. We generated 229 out of the possible 256 model treatment permutations. Each model treats the data differently, for example by using different spectral regions before data compression, thus each model should be expected to give different performance values. We purposely chose individual treatments that were expected to give some classification ability, based on various reports in the literature.
  • a performance metric (the area under the receiver operating characteristic curve; AUC) for each model treatment was computed.
  • AUC area under the receiver operating characteristic curve
  • a PP threshold for normal class membership was first established, and samples with a PP above this value were classified as normal. For example, if the threshold was set to 0.2 and the sample PP was 0.23 (23% probability of being normal), the sample's class as predicted by the model was normal. These 56 predicted classes were compared to the true classes, and the fractions of abnormal samples correctly classified (true positive rate) and normal samples misclassified (false positive rate) by the model were computed. These rates were computed as the PP threshold was varied from 0 to 1 in increments of 0.05.
  • FIG. 4 is an example of a Receiver Operating Characteristic Curve (ROC curve) generated from an individual model treatment, which has an AUC of 0.74.
  • FIG. 5 shows the individual AUC performance metrics (computed using the median PP for each sample) for each model treatment.
  • the AUCs vary from less than 0.5 (no classification ability) to 0.78.
  • the current screening method for cervical cancer Pap smear followed by visual assessment of cells by a cytotechnologist and a pathologist
  • a classification model is trained using a finite amount of data. Because of this, there will be uncertainty in the model's predictive ability, leading to a decrease in the claimable model accuracy. For example, a test sample whose predicted value is close to the boundary that is used to determine class membership will have a high degree of uncertainty associated with its predicted class. Bundling models reduces this uncertainty. Bundling therefore can allow a higher percentage of samples from the entire population to be predicted with confidence.
  • a single classification model may provide acceptable accuracy for one subset (subset 1) of all possible samples, but may perform poorly for another subset (subset 2). Likewise, another model that emphasizes different spectral features or makes different assumptions about the distribution of classes may perform well on subset 2 but not on subset 1. Combining the outputs of these two models will therefore improve accuracy over the entire sample population.
  • Bundling Bundling the output of multiple models was performed at two levels as shown in FIG. 3).
  • the first bundling level combined the 13 bootstrap results for each sample within each model treatment by simply taking the median PP of each sample. We then had 1 PP for each of the 56 samples and each treatment.
  • a performance metric the area under the receiver operating characteristic curve; AUC) for each model treatment was then computed, as it was used in the second level of bundling.
  • the second bundling level combined the median PP (calculated within each model treatment) for each sample across model treatments.
  • the 17 models with the highest individual AUC performance metrics were chosen as candidates for bundling (see FIGS. 3 and 5).
  • Up to 11 model treatments were bundled as follows. First, a PP data matrix was formed for the 56 samples (rows) and 17 candidate models (columns). The 17 ⁇ 17 correlation coefficient matrix of the PP matrix was computed, and the two models treatments with the smallest correlation between the PPs for each sample were chosen for bundling. These two model treatments were removed and the selection process was repeated 5 more times. This yielded from 2-12 model treatments to bundle; the remaining description illustrates the 11-treatment bundling case.
  • the performance of the 11 bundled models was evaluated using the AUC metric as well.
  • For each PP threshold majority voting among 11 PP values for each sample was used to specify the predicted class. For example, if the threshold was 0.2, and 6 or more of the PPs were greater than 0.2, the sample was classified as normal. As before, the PP threshold was swept from 0 to 1, predicted classes were compared to true classes, true and false positive rates were calculated, and the AUC metric was computed.
  • Other combinations of models can also be used. For example, certain models can be accorded greater or lesser weight, perhaps dependent on their performance on certain types of samples, in a voting scheme. Some models can be combined arithmetically, e.g., mean or median, before combination with other models. Patterns in the outputs of the models can also be used to derive the classification. Each vote in a voting scheme can also be weighted by its probability or confidence level. The models can also be combined after evaluation against thresholds.
  • the second level encompasses a much broader scope by bundling across model treatments.
  • the 17 model treatments with the highest individual AUCs were chosen as candidates for bundling. This down selection process ensures that the bundling operation begins with data that is useful on its own. However, bundling models that have identical performance on each test sample would not change the accuracy, as all model results are perfectly correlated. We therefore down selected further by choosing model treatments whose performances were good, but not identical.
  • FIG. 6 shows how the AUC improves with bundling across model treatments.
  • the AUCs for a single model treatment (first level bundling) ranged from 0.54 to 0.79.
  • first level bundling we choose 165 different combinations of 3 out of 12 possible models and computed the AUC for each.
  • the 3-model bundling case yielded AUCs ranging from 0.56 to 0.91, a statistically significant improvement over the 11 individual model results.
  • the bundled AUC continued to improve with number of models bundled.
  • FIG. 7 illustrates the ROC curve generated after 11 models were bundled together.
  • Within-sample variance classification can also be bundled with other methods. For example, models can be generated using within-sample mean spectra. These models can then be bundled together with the models generated from the within-sample variance (e.g., standard deviation) spectra to improve the classification accuracy over either method.
  • within-sample variance e.g., standard deviation
  • FIG. 8 illustrates the individual AUC values for all 573 model treatments.
  • the 14 model treatments with the highest individual AUCs were chosen as candidates for bundling.
  • the ROC curve is plotted in FIG. 9 for the case of 11 treatments bundled, resulting in an AUC value of 0.91.
  • test PP threshold would be fixed.
  • sensitivity fraction of abnormal samples detected
  • specificity fraction of normal samples detected

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JP6976257B2 (ja) * 2016-01-28 2021-12-08 シーメンス・ヘルスケア・ダイアグノスティックス・インコーポレーテッドSiemens Healthcare Diagnostics Inc. マルチビューの特徴付けのための方法及び装置
EP3408651B1 (fr) 2016-01-28 2024-01-10 Siemens Healthcare Diagnostics Inc. Méthodes et appareil de détection d'interférant dans un échantillon
CN109459409B (zh) * 2017-09-06 2022-03-15 盐城工学院 一种基于knn的近红外异常光谱识别方法
EP4202413B1 (fr) * 2021-12-21 2024-01-31 Bruker Optics GmbH & Co. KG Procédé mise en oeuvre par ordinateur pour prédire des propriétés d'échantillons sur la base de mesures spectrales et système informatique pour cela

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